Institution
Tokyo Institute of Technology
Education•Tokyo, Tôkyô, Japan•
About: Tokyo Institute of Technology is a education organization based out in Tokyo, Tôkyô, Japan. It is known for research contribution in the topics: Thin film & Catalysis. The organization has 46775 authors who have published 101656 publications receiving 2357893 citations. The organization is also known as: Tokyo Tech & Tokodai.
Topics: Thin film, Catalysis, Polymerization, Laser, Phase (matter)
Papers published on a yearly basis
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Gyeongsang National University1, University of Tokyo2, University of Tsukuba3, University of Cincinnati4, University of Sydney5, Peking University6, Budker Institute of Nuclear Physics7, Polish Academy of Sciences8, University of Maribor9, National Taiwan University10, National Central University11, Chonnam National University12, Sungkyunkwan University13, Princeton University14, University of Melbourne15, Virginia Tech16, Nagoya University17, Tata Institute of Fundamental Research18, University of Ljubljana19, Osaka University20, Nara Women's University21, École Polytechnique Fédérale de Lausanne22, Tohoku Gakuin University23, Yonsei University24, Korea University25, Chiba University26, Niigata University27, Tokyo Institute of Technology28, Kyungpook National University29, Goethe University Frankfurt30, Seoul National University31, University of Science and Technology of China32, Tokyo Metropolitan University33, Austrian Academy of Sciences34, Osaka City University35, Tokyo University of Agriculture and Technology36, Toho University37, Kanagawa University38, Panjab University, Chandigarh39, Saga University40, National United University41, Tohoku University42
TL;DR: In this paper, the authors presented a method to solve the problem of the EPT problem in PhysRevLett, a journal published on 2010-11-05, modified on 2017-12-10.
Abstract: Reference EPFL-ARTICLE-154584doi:10.1103/PhysRevLett.94.182002View record in Web of Science Record created on 2010-11-05, modified on 2017-12-10
299 citations
TL;DR: In this paper, a series of laboratory tests are carried out on the friction between steel and air-dried sands with a simple shear apparatus, and the significance of factors on the frictional coefficient are examined with the use of the experimental design method by orthogonal array table.
299 citations
TL;DR: It is found that thermally reduced graphene oxide offers the most favorable electrochemical performance among the different materials studied and has a profound impact for the applications of chemically modified graphenes in electrochemical devices.
Abstract: Electrochemical applications of graphene are of great interest to many researchers as they can potentially lead to crucial technological advancements in fabrication of electrochemical devices for energy production and storage, and highly sensitive sensors. There are many routes towards fabrication of bulk quantities of chemically modified graphenes (CMG) for applications such as electrode materials. Each of them yields different graphene materials with different functionalities and structural defects. Here, we compare the electrochemical properties of five different chemically modified graphenes: graphite oxide, graphene oxide, thermally reduced graphene oxide, chemically reduced graphene oxide, and electrochemically reduced graphene oxide. We characterized these materials using transmission electron microscopy, Raman spectroscopy, high-resolution X-ray photoelectron spectroscopy, electrochemical impedance spectroscopy, and cyclic voltammetry, which allowed us to correlate the electrochemical properties with the structural and chemical features of the CMGs. We found that thermally reduced graphene oxide offers the most favorable electrochemical performance among the different materials studied. Our findings have a profound impact for the applications of chemically modified graphenes in electrochemical devices.
299 citations
09 May 1995
TL;DR: It is shown that the parameter generation from HMMs using the dynamic features results in searching for the optimum state sequence and solving a set of linear equations for each possible state sequence.
Abstract: This paper proposes an algorithm for speech parameter generation from HMMs which include the dynamic features. The performance of speech recognition based on HMMs has been improved by introducing the dynamic features of speech. Thus we surmise that, if there is a method for speech parameter generation from HMMs which include the dynamic features, it will be useful for speech synthesis by rule. It is shown that the parameter generation from HMMs using the dynamic features results in searching for the optimum state sequence and solving a set of linear equations for each possible state sequence. We derive a fast algorithm for the solution by the analogy of the RLS algorithm for adaptive filtering. We also show the effect of incorporating the dynamic features by an example of speech parameter generation.
299 citations
298 citations
Authors
Showing all 46967 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Matthew Meyerson | 194 | 553 | 243726 |
| Yury Gogotsi | 171 | 956 | 144520 |
| Masayuki Yamamoto | 171 | 1576 | 123028 |
| H. Eugene Stanley | 154 | 1190 | 122321 |
| Takashi Taniguchi | 152 | 2141 | 110658 |
| Shu-Hong Yu | 144 | 799 | 70853 |
| Kazunori Kataoka | 138 | 908 | 70412 |
| Osamu Jinnouchi | 135 | 885 | 86104 |
| Hector F. DeLuca | 133 | 1303 | 69395 |
| Shlomo Havlin | 131 | 1013 | 83347 |
| Hiroyuki Iwasaki | 131 | 1009 | 82739 |
| Kazunari Domen | 130 | 908 | 77964 |
| Hideo Hosono | 128 | 1549 | 100279 |
| Hideyuki Okano | 128 | 1169 | 67148 |
| Andreas Strasser | 128 | 509 | 66903 |